High-speed rail services development and regional accessibility restructuring in megaregions: A case of the Yangtze River Delta, China

Lei Wanga,b

aManchester Urban Institute, School of Environment, Education and Development, The University of Manchester, Manchester, UK

bKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, CAS, Nanjing, China

Abstract: Many countries have planned to improve intercity links in megaregions by developing High-speed rail (HSR) projects. The development of HSR service network in megaregions can change accessibility landscapes and create advantaged and disadvantaged cities. This study investigates how HSR service development has restructured uneven regional accessibility by developing a new regional accessibility measurement to incorporate train service frequency into the prevailing speed-dominated calculations. We conduct a longitudinal study to examine the changing patterns and relevant determinants of regional accessibility in the Yangtze River Delta (YRD), China, as well as the differences between conventional rail (CR) cities and HSR cities. The results indicate that the development of HSR services network has enhanced regional accessibility in the YRD rather than having the overall effect of restructuring the existing accessibility patterns formed by CR services. It has reduced the disparity of regional accessibility but has widened the gap between HSR and CR cities and increased the disparity of CR cities. Combined with a Tobit regression analysis, the results suggest that HSR network development in the YRD was highly spatially selective towards the cities that had higher CR network accessibility and better economic development performance. However, the disadvantage of regional accessibility for CR cities remains persistent, which may result in the issue of ‘peripheralisation of the periphery’ on the CR cities. Some policy implications for an integration of the CR and HSR services for both CR and HSR cities are outlined in conclusion.

Keywords: High-speed rail services; conventional rail services; uneven regional accessibility; HSR and CR cities; the Yangtze River Delta

1 Introduction

Since the operation of the first commercial high-speed rail (HSR) service in Japan in 1964, the transport mode has attracted widespread attention from transport planners and policy-makers. After more than a half-century’s development, HSR has become one of the most important inter-city transport modes for its reliability and high-quality service (Campos and de Rus 2009, Givoni and Banister 2012). Worldwide experience indicates that the HSR train is the most competitive transport mode for distances of around 200-800 km (Givoni 2006), which is within the scope of megaregions. Even for medium to long travel distances, HSR services have reduced airlines’ market shares, frequencies and ridership (Fu, Zhang and Lei 2012, Xia and Zhang 2016, Chen 2017). Higher population density and increasing intercity connections rationalise the plans for an HSR network in megaregions.

By reducing intercity travel time and distance, HSR can transform regional accessibility (Ureña, Menerault and Garmendia 2009). However, the HSR train is not just another fast transport mode as it can also have a significant impact on territorial issues and can reshape spatial structures (Hall 2009,

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Vickerman 2018). Considering that the introduction of HSR can cause uncertainties in different localities, it often generates heated public debates concerning important issues such as where and how HSR should be developed and which areas may benefit (or lose out) from it. The issues confronting policymakers and professionals are more pragmatic and specific; for instance, should a new passenger dedicated HSR be built or should the conventional rail (CR) be updated to adapt with HSR trains? What the new route and how many stations should be built? How will HSR services coordinate with the well-established CR services network? How can the new rail service be planned to achieve optimal regional efficiency and equity of accessibility? What is the risk of peripheralisation of certain areas? These challenging issues remain controversial, even though there have been massive HSR expansions globally since the turn of the millennium. Subsequently, there is a need for a more critical analysis that explores the far-reaching impacts of HSR services so that future HSR planning is well informed. For instance, discussions on HSR2 in the UK have been ongoing for almost 10 years while policymakers and professionals consider how this mega project would bring accessibility benefits and associated economic changes to cities in England, especially those in the lagging-behind Northern regions (Martínez and Givoni 2012).

Previous studies have reported that HSR networks were playing a critical role in the promotion of economic integration in megaregions (Cheng 2010, Cheng, Loo and Vickerman 2015). However, empirical studies have revealed that HSR development has had negative impact on non-central cities (Monzón, Ortega and López 2013). For instance, the South Korean HSR has contributed to an increase in the spatial imbalance of accessibility in the country (Kim and Sultana 2015). The HSR networks in Europe and China have increased the disparity of accessibility between the central and peripheral areas (Givoni 2006, Shaw et al. 2014). Furthermore, rail cities outside HSR networks have been disadvantaged and marginalised (Monzón, Ortega and López 2013, Jiao et al. 2014). However, it is difficult to compare and contrast many of the previous case studies because multiple measurements and data sources were used at different spatial scales (Martínez and Givoni 2012, Jiao et al. 2014, Kim and Sultana 2015). Each study’s conclusion was associated with the particular HSR development strategies of the specific country used in the case study. Previous research has also identified three HSR development strategies, involving corridors, hybrids and networks (Perl and Goetz (2015). Studies (Jiao et al. 2014, Kim and Sultana 2015) have modelled accessibility surface change by comparing scenarios with and without HSR infrastructures in the geographic information systems (GIS) environment. However, these analyses often pay insufficient attention to train services in real travel time when, in reality, the uneven accessibility pattern among different cities has persisted throughout rail development (Loo and Comtois 2015). This study differs from previous research because we conduct a comparative temporal analysis rather than exploring a snapshot at a specific point in time. In addition, this research importantly investigates the extent to which HSR services have restructured the uneven accessibility landscape given that CR networks existed and were well developed before HSR development began in many countries.

Compared with its counterparts, China began developing HSR relatively late, but its HSR networks have increased rapidly over the past decade. By the end of 2016, China had developed 22,000km of HSR networks including those upgraded from CR lines. According to the Mid to Long Term Railway Development Plan in China (2016), China’s HSR network will expand to a length of 38,000km by 2025 and will connect all cities with a population of 500,000 and above. The continual expansion of its HSR network makes the country not only a laboratory for exploring the impacts of HSR services,

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but also a case for reflecting on this approach and pace of developing HSR. The megaregion of the Yangtze River Delta (YRD), in particular, has been one of the Chinese government’s key HSR projects; 16 networks are in operation and more are in planning. With a well-established CR service network, the region offers an excellent opportunity to study the change in train services from the pre-HSR period to the current HSR period. This ex-post examination of HSR services in the YRD also provides implications for other countries and regions with regard to their HSR planning and construction.

Through focusing on the changes in regional accessibility from the development of HSR services network in the megaregion, this paper aims to contribute to the literature and policy debates in the following ways: (1) to develop a new measurement of the city’s regional accessibility by integrating rail travel time and train service frequency; (2) to examine the changes in regional accessibility and advantaged and disadvantaged cities; (3) to explore the determinants of changing accessibility patterns and relevant policy implications on the development of HSR service network in megaregions. To achieve this, we employed a multi-spatial scale and initiated the analysis at the lowest administrative unit at which the Chinese government develops rail infrastructures and manages train services (the county level) to explore the varied impacts among various rail cities in the YRD.

This rest of this paper is structured as follows: section 2 provides a brief literature review, section 3 describes the research methodology, section 4 introduces the study area and data collection methods, and section 5 presents the empirical results of regional rail accessibility patterns and the factors that influenced these patterns. Finally, this paper concludes by highlighting the key findings, discussing limitations, and offering suggestions for policymaking and future research.

2 Literature review

The idea of an HSR network construction often crouches on the assumption that new patterns of urban and regional development will be generated by transforming spatial accessibility and connectivity (Monzón et al. 2013, Wong and Webb 2014). In addition, previous HSR studies have highlighted HS R’s reduced travel time and associated increase in time-based accessibility (Martínez and Givoni 2012, Kim and Sultana 2015). With regard to existing studies, two research approaches are typically used. The first simulates the accessibility surface based on the transport network by assuming fixed travelling speeds for different transport modes to simulate the reduction in travel time or the location-based daily accessible distance (Wang et al. 2009, Monzón et al. 2013, Jiao et al. 2014, Wang and Duan 2018). The second approach uses the timetable of train schedules to explore changes in intercity accessibility that result from HSR services development (Shaw et al. 2014, Zhang and Derudder 2016). For instance, complex network analysis/ graph theoretical analysis has been used to measure the node degree/centrality, closeness and strength over the development HSR network and train services (Chen et al. 2018, Xu, Zhou and Qiu 2018, Gong et al. 2018). These indicators are calculated to reflect the hierarchical change of cities from various dimensions. Despite different approaches and measurements are used in various studies, many results indicate that an enlarged accessibility disparity caused by HSR services development between the hub of transportation and other cities has been widely acknowledged (Ureña et al. 2009, Monzón et al. 2013, Ng et al. 2018). Previous studies suggest that the new transport mode is in favor of major cities at the regional scale.

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When an HSR network is in operation, it can create either spatial advantages or disadvantages for a city through what has been termed the ‘siphoning effect’ (Wang et al. 2012). Blum, Haynes and Karlsson (1997) and Gutiérrez, González and Gómez (1996) found that a newly-opened European HSR line caused an increase in disparity between hub cities and peripheral areas, which resulted in a core-periphery accessibility structure. Further, a case study in Spain revealed that cities with a poor initial situation enjoyed the greatest accessibility gain in absolute terms, while cities with a favourable initial situation experienced relatively better overall accessibility, which caused further spatial polarisation (Monzón et al. 2013). A similar study in China also revealed that HSR development could lead to substantial improvements in accessibility and national time-space convergence, as well as increasing accessibility inequality for cities in the eastern, central and western regions (Jiao et al. 2014). Additional effects from the emergence of HSR networks include the corridor effect and core-periphery patterns of accessibility (Shaw et al. 2014). In South Korea, Kim and Sultana (2015) reported that HSR development degenerates the spatial equity of accessibility because it was prioritised in the already-advantageous Seoul capital area. About 60% of the Korean population can access an HSR station within a half-day of travel time (Kim, Sultana and Weber 2018). The development of frequent HSR services in South Korean has resulted in a significant decrease in domestic aviation demand (Park and Ha 2006). In Japan, Clever and Hansen (2008) also reported the negative impact of HSR’s entry on domestic air services, while they noticed that the development of Shinkansen had increased the share of rail services gradually in domestic transportation. By conducting a comparative study between HSR development in Japan, South Korea and China, Wan et al. (2016) found that HSR service speeds, distances, and corresponding impacts vary across countries. Since its impact on accessibility varies across different cities and scales, various scholars have emphasised the need for a multi-level analysis to differentiate the consequences for small and large cities (Ureña et al. 2009, Wang, Xu and He 2013).

Moreover, concerns about time-dominated accessibility have also been raised (Campos and de Rus 2009, Givoni and Banister 2012, Moyano, Martínez and Coronado 2018). Methodologically, the speed-related accessibility measurement re-emphasised the accessibility pattern created by the CR network. Also, the operating speed of HSR trains usually doubles or triples that of CR trains. However, the speed is offset by stops along the journey, which is a point that is often neglected in discussions. In reality, it is assumed that each additional stop adds 5-10 minutes of journey time to accommodate the train’s deceleration and subsequent acceleration, as well as the time allocated for passenger loading and unloading. Therefore as the number of stops increases, the average travel speed of an HSR train can be much lower than its actual operating speed. Moreover, compared to CR services, HSR is more than just an improvement in speed. The increased track speed has also significantly improved the capacity of rail services and the associated frequency of train movement. However, the train service frequency reflects waiting time, which is usually neglected during accessibility evaluation, even though it is an important factor in determining intercity rail accessibility (Wang et al. 2013).

Finally, accessibility evaluations should also consider the local context of rail service changes. For instance, previous research has found that European CR services have continually been substituted by an increasing number of HSR services (Givoni 2006), whereas, in China’s HSR networks, there have been moderate improvements in both the speed and service frequency of CR services during the rapid HSR development (China Railway Corporation 2016). Since China is rapidly urbanising, many

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travellers still prefer the relatively affordable prices and the large number of stations associated with the CR train service (Long, Meng and Li 2016). A recent survey has also revealed that Chinese travellers are mindful of both the time and cost of the train service (Wang et al. 2013). These findings suggest that the demand for rail travel is more complex in China than in Western countries. The nuanced demand structure in China has a definite effect on the patterns of CR and HSR services development. Intercity rail accessibility in China should, therefore, be evaluated by examining the interplay of CR and HSR services development.

A review of the literature shows that the comparability of previous studies remains questionable as they have focused on different spatial focuses and data sources. For instance, studies in Korea (Kim and Sultana 2015), Japan (Brunello 2018) and the UK (Martínez and Givoni 2012) focused on the corridor effects of HSR development, while studies in Spain (Monzón et al. 2013) and China (Jiao et al. 2014) emphasised the core-periphery effect. In addition to the different strategies of HSR development, previous accessibility studies were dominated by travel time saving or the train speed increase from HSR development. Aside from a few studies on big cities (Zhu, Zhang and Zhang 2018, Shaw et al. 2014), research has rarely combined the changes in train service frequency with an accessibility analysis for all rail cities at the regional scale. Therefore, this paper will also contribute to the research that examines HSR network change over time in the scale of megaregions.

Another important issue in rail development analyses concerns which analytical unit is selected for the research project (Banister and Thurstain-Goodwin 2011). This is particularly critical in China as studies have tended to use different administrative levels1 to define cities (Ma 2005). In China, the county is the lowest administrative level permitted to construct rail stations and to arrange train services. The prefecture and higher levels usually have large territories, including a central city at the prefectural level, and several county-level cities and counties. Previous studies (Jiao et al. 2014, Shaw et al. 2014) highlighted higher level cities’ (prefecture and above levels) benefits from HSR development, but overlooked the change in lower-level cities’ (county-level) welfare and rail accessibility inequality within the prefecture. HSR services are highly polarised in higher-level cities, because most HSR trains only stop in populated urban areas to maintain a high-speed rate while also ensuring that they can accommodate the maximum number of passengers (Qin 2016). Even though small cities spend a lot of capital to build stations, relatively few HSR trains stop in these locations (Moyano et al. 2018). In addition, some CR stations at lower level cities have been abandoned for passenger services to pave the way to HSR development in neighbouring large cities, which may decrease their accessibility at the regional level (Zhu, Yu and Chen 2015). For these reasons, this study’s basic analytical unit begins at the county level to analyse changing regional accessibility for all rail cities and relevant determinants across the megaregion.

3 Research methods

3.1 Regional accessibility measurements

In this paper, we calculated intercity rail accessibility by integrating train travel time and train service frequency. Both dimensions are important time-related factors for measuring a city’s accessibility within its regional rail network (Qin 2017, Wang et al. 2013). Train travel time is considered an intuitive measurement that corresponds to a subjective perception of distance, while train service

1 Chinese cities are designated into different hierarchical levels; from provincial, vice-provincial, prefectural to county level.

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frequency indicates the time spent waiting at stops when travelling from one city to another.

We employed a widely-used location-based measurement to calculate regional accessibility (Gutiérrez 2001, Jiao et al. 2014), which is quantified by a given city’s total travel time and total train connections to a group of important destination cities in the region. Specifically, we used three groups of destination cities that included Shanghai (the economic centre of YRD), the provincial capitals such as Nanjing, Hangzhou and Hefei (provincial political and economic centres), and the prefecture-level cities with train services (a group of 24 cities i.e. Suzhou, Wuxi, Jiaxing, Wuhu etc., that form the sub-regional economic centres of prefectures). These 28 destination cities are geographically scattered in the YRD, which is a good benchmark for calculating regional accessibility. Using a similar approach as Wang et al. (2013) and Zhu et al. (2018), we developed an index of regional accessibility score (RAS) by integrating the rail travel time and the train service frequency to the destination cities. The formulas and calculation steps are described in the following paragraphs.

We separated HSR service network from CR service network in order to examine different accessibility patterns created by the CR and HSR services in 2007 and 2016. However, for the CR cities (such as Yancheng, Huaian, Bangbu, etc.) that did not have HSR services in 2016, we treated these cities by connecting their CR stations to the nearest corresponding HSR stations and added additional 30 minutes penalty to compensate for the time it takes to connect to the HSR network. Then, we modelled cities’ regional accessibility scores for CR services (in 2007 and 2016) and HSR services (in 2016) separately with the various equations that are shown in the next several pages. We began by calculating the respective average daily travel times (T ij) of HSR or CR services between any two cities that have direct train services. Using an indicator of average travel time is essential because the same type of trains can have different calculated average speeds as they do not make the same number of stops. Table 1 provides examples of trains operating between the Nanjing Station and the Shanghai Station, and it illustrates that the HSR train G7037 has a calculated average speed that is only 11.8% faster than the fastest CR train Z195. However, on average, HSR trains save more than 50% travel time than CR trains do.

T ij=∑nij=1

nij

tnij

ij

nij

(1)

where nij represents the number of daily direct trains of CR services or HSR services between the two cities, and t nij

ij represents the travel time of a CR or a HSR train service between city i and city j.

To keep the calculation simple, the travel time between different stations within the same city was ignored if the city had more than one CR or HSR station.

Table 1 Examples of train services between the Nanjing Station and the Shanghai Station in 2016

Train No. Type of trainTravel time

(hour)Number of

intermediate stopsCalculated average

speed (km/h)

G3 HSR 1.65 0 264G7037 HSR 2.23 6 135D3089 HSR 2.35 3 128D2281 HSR 2.50 7 120Z195 CR 2.53 1 119Z163 CR 2.85 1 106

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T115 CR 2.95 2 102T203 CR 3.72 4 81K281 CR 3.28 2 92K233 CR 5.35 4 56

Subsequently, the accessibility index between any two cities by CR trains or HSR trains ( AI ij), which is shown in equation (2), was measured by combining the average travel time and train service frequency per day. The frequency of intercity train services reflects the waiting time for train services. More frequent train services implies less waiting time, and vice versa. For instance, if there is only one train operating between two cities per day, and if a traveller misses the train on that day, the waiting time would be one day. Revised from Wang et al. (2013), the waiting time for train services is used as a weight to the average intercity travel time to obtain an intercity accessibility index. The application of square root transformation is used to dampen the weighting treatment, which is similar to an approach used by Zhu et al. (2018) to calculate intercity airline connectivity. In general, this function allows routes with a higher train service frequency to have higher accessibility scores, and allows routes with a longer train travel time to have smaller accessibility indices, ceteris paribus. In addition, our calculation excluded situations with no direct train service between two cities because of the resulting lack of intercity accessibility.

AI ij=√ nij

24 T ij

(2)

The total train connections (TTCi), the total travel time (TTTi), and the accumulated accessibility index (AAIi) of city i to the 28 central cities by CR or HSR services in the YRD were calculated with equations (3), (4) and (5) respectively (note: if there is no direct train service between city i and central city k, then the nearest transfer city was used by imposing a 30-minute time penalty, which guarantees that the route of minimal travel time was selected).

TTCi=∑k=1

k

f ik (3)

TTT i=∑k=1

k

T ik (4)

AAI i=∑k=1

k

AI ik (5)

In the above formulas, fik, Tik and AIik indicate the frequency of direct CR or HSR train services per day, average CR or HSR travel time, and the accessibility index by CR or HSR services between a city i and a central city k in the YRD, respectively. The three indicators were calculated via network analysis in the ArcGIS environment.

The RAS of city i by CR or HSR services was then measured by a normalised AAIi for the total

sample of cities. The normalisation of min-max scaling was used to assign a value of 0 to the minimum value and a value of 1 to the maximum value. All values were scaled into the range [0,1]. By using this normalisation, we were able to conduct a temporary comparison of the changing accessibility scores among cities before and after HSR services.

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RASi=Normalised ( AAI ¿¿i)¿ (6)

Normalised ( AAI ¿¿ i)={ AAI i−min ( AAI i ) }/ {max ( AAI i )−min ( AAI i )}¿ (7)

max(AAIi) and min(AAIi) are the maximum and minimum values of a set of original cities’ accumulated accessibility indices to all central cities.

Consequently, three patterns (the CR2007 network, the CR2016 network, and the HSR2016 network supplemented by the CR2016 network, hereafter, ‘Rail2016’) of TTT, TTC, and RAS were calculated to examine the changes in regional accessibility over the decade of HSR service network development in the YRD.

3.2 Multi-regression analysis of regional accessibility

An upgraded and developed rail network provides an important underpinning infrastructure to urban and regional development. Despite the importance of regional accessibility improvement, its relationship to the travel demand pattern of a particular group of cities is also critical to rail infrastructure planning and train schedule management. Thus, we attempted to identify the attributes of the urban system to explain not only the changes of relations with regard to accessibility patterns over time but also the difference between different rail city groups, i.e., HSR cities and CR cities. According to a framework used by Moyano et al. (2018) and Yang et al. (2018), the determinants of rail services and associated accessibility in the urban system can be categorised into three groups, namely, the nature of infrastructure, urban socioeconomic performance and institutions. Additionally argument that the pattern of train services is path dependent in nature along rail network development (Jiao, Wang and Jin 2017). Thus, we also controlled for values of regional accessibility in 2007 in analysing the determinants for the patterns of regional accessibility in 2016.

When dependent variables are censored or limited in their ranges, the Tobit regression model is superior to the ordinary least squares (OLS) regression estimator because the latter may result in inconsistent and biased parameter estimates (McDonald and Moffitt 1980). In this study, a two-limit Tobit model (Rosett and Nelson 1975) was used because the dependent variables of regional accessibility are standardized into the value range [0, 1]. The two-limit Tobit model has been widely applied in statistical analysis; subsequently, we were able to follow the relevant methodological steps conducted by Wang et al. (1997) and Ma and Liao (2018). We employed this model to account for the changing patterns of regional accessibility and its two components by the aforementioned factors. In addition, we also employed the same min-max scaling approach to standardise all variables into the value range [0,1].

4 Study area and data collection

4.1 Study Area

The YRD is located on the shores of the East China Sea. It is one of the most developed megaregions in China and serves as a regional focal point for the country’s HSR development plans (Figure 1). The YRD spans a territory of 349,439km² and is economically centred by Shanghai. It consists of the four provinces of Shanghai, Jiangsu, Zhejiang, and Anhui and is home to 42 prefectures2. In 2016,

2 A prefecture includes a prefecture-level city and several of its administrated county-level cities and rural counties. The territory of a prefecture is similar to that of the city-region in English literature.

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the YRD had a population of 222.04 million and a GDP of 2,622.03 billion USD. However, there were major economic disparities among the four provinces (including across their own administrated cities and counties), with wealth that originated in the geographical east and dwindled when moving towards the west. Shanghai has been the most developed province-level municipality with a GDP per capita of 17,089 USD, followed by Jiangsu (14,321 USD), and Zhejiang (12,519 USD). Anhui has long been the poorest province in the YRD, accounting for only USD 5,860 of the total regional wealth. Figure 1 shows the uneven distribution of GDP per capita at the city and county level in 2015. The regional GDP had a rather high coefficient of variation (CV) of 0.628. Historically, the cities surrounding Lake Tai have been the most developed in the YRD, and sometimes in the whole of China.

The China Railway Corporation and its 18 local branches are responsible for the management duties of the rail networks and train services in China. All of the 18 local branches are state-owned enterprises, with the YRD’s Shanghai Railway Bureau branch administrating one of the largest and busiest rail networks in the country. The YRD HSR network has been rapidly developed over the past decade. Although its first HSR service (from Nanjing to Hefei) only began operating in April 2008, the network had expanded to a total of 16 services by October 2016. With the exception of the CR-to-HSR upgrade of the Nanjing-Nantong service, the YRD HSR network was newly constructed to primarily accommodate passenger services. With a total length of over 3,700 km, the YRD HSR network serves 33 of the 42 cities at prefecture and above levels, and 63 of the 171 county-level cities. In addition to the robust HSR network, the CR network is also well developed in YRD, with a 5,400 km length and services to 93 cities. Among the cities in the YRD’s rail network, 54 offer both CR and HSR services. The YRD’s substantial CR and HSR service networks provide an ideal setting to study the developmental interplay between the two and to monitor changes in regional accessibility.

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Figure 1 Location of the study area

4.2 Data collection

A train timetable, created through market-driven factors and government intervention, is an important data source for exploring intercity connectivity and accessibility. The China Railway Corporation and its local branches actively adjust train timetables to accommodate changes in traffic demand and rail construction. In this paper, we compared regional accessibility of the YRD before and after the HSR era by obtaining official train timetables from both time periods. The first timetable, dated 18 April 2007, was published just after the sixth speed-up campaign made by the China Railway Corporation. The average train operating speed in China at that point was 70.18km/h, which was an increase of 7%. Some of the backbone lines such as Beijing-Shanghai, Beijing-Harbin, and Beijing-Guangzhou had trains travel faster and reached speeds of around 200km/h by eliminating stops in intermediate cities. There were no HSR trains in the YRD at the time. The second train timetable, dated24 October 2016, places it well within the YRD’s 16-line HSR era.

CR and HSR trains in China are generally operated on different tracks. CR trains operate at speeds below 120 km/h, while the HSR train service offers two speed categories. The first category includes trains operating at a speed of 200-250 km/h. The second category includes trains that operate at speeds of 300-350 km/h. In this paper, we considered multiple CR and HSR stations within a city as either a CR or an HSR station and excluded train connections between stations within the same city from the analysis. The 2007 timetable contained 15,012 records of intercity train connections per day in the YRD. By the time the 2016 timetable was published, the number had increased to 48,375 intercity connections per day, which can be subcategorised into 13,930 CR connections, and 34,445 HSR connections. The numbers reveal that the frequency of the intercity train network service had

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increased by 3.2 times in just under a decade. While CR connections in the YRD had decreased by 7.7%, HSR had become the dominant train service.

For the multi-regression analysis, we first use a dummy variable of whether or not a given city has an HSR station to represent its nature in the rail network. The operation speed of HSR trains is twice faster than that of CR trains. Thus, HSR cities are more accessible than CR cities, ceteris paribus. HSR development is highly selective spatially for those cities with better economic development or higher national strategic importance (Wang et al. 2013). Many HSR lines have been developed along the busiest CR lines. For instances, the Beijing-Shanghai, Nanjing-Shanghai and Shanghai-Hangzhou-Ningbo HSRs were all built along the old CR lines. The development strategy does not only release the capacity of CR lines for cargo transport, but also secure ridership for HSR lines to generate revenue (Yin, Bertolini and Duan 2015). Therefore, we hypothesise that HSR cities perform better in accessibility scores even in the pre-HSR period. Indicators for urban socioeconomic performance include: GDP, urban residents and GDP per capita, which are widely employed in literature to predict the potential accessibility demand from both total and individual levels (Preston 2001, Chow et al. 2006). Lastly, the institution factor is represented by the hierarchical urban administration system. Higher level cities are usually regional economic centres and transport hubs. The hierarchical level of cities is largely associated with its rail network status. Thus, we adopt cities at different levels as a key factor of regional accessibility (Table 2).

Table 2 Definitions and descriptions of variables

Categories Variables Description and unit2007 2016

Mean SD Mean SD

Accessibility variables

TTTCity’s total rail travel time to 28 destination cities (hour).

97.32 34.35 54.41 30.81

TTCCity’s total rail travel service frequency to 28 destination cities.

377.8 513.4 897.6 1056.5

RAS City’s regional accessibility score. 0.63 0.22 0.75 0.19

Independentvariables

HSR city or not

1 for city with an HSR station or more; 0 for others.

0.63 0.48 0.63 0.48

City levelCity’s hierarchical level: 1 = provincial city, 2 = vice-provincial city, 3 = prefectural city and 4 = country level city.

3.65 0.56 3.65 0.56

Population City’s residents (million) 1.08 1.23 1.22 2.15

GDPCity’s gross domestic product (billion yuan)

27.9 84.86 96.70 244.81

GDP per capita

City’ GDP per capita (thousand yuan) 19.07 17.13 62.20 38.28

5 Empirical results

5.1 Patterns of RAS components

TTT

Figure 2 shows three TTT patterns for CR and HSR services in 2007 and 2016 by using a unified legend to group cities. The patterns show that both rail station presence and geographical location

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played important roles in a city’s TTT at the regional scale.

In 2007, the lowest TTT value was found in cities (i.e., Nanjing, Zhenjiang, Wuxi, and Caohu) located in Jiangsu and Anhui provinces in the geographic centre of the YRD. Some geographically peripheral cities (e.g., Xuzhou, Fuyang, and Jinhua), although located at the crossing paths of multiple rails, had low TTT values that indicated a core-periphery structure of CR train services in 2007.

After a decade of train services development in 2016, it is evident that cities with higher levels of accessibility were clustered into a prominent belt between Shanghai and Hefei. The introduction of HSR services compressed intercity travel time and TTT in general, but the pattern of CR services underwent little change (r=0.981, p<0.01) over the decade. Lower TTT value corridors and enclaves also emerged along HSR lines in the periphery areas, indicating that HSR services have weakened the core-periphery structure of travel time accessibility, and they provide a way to lessen the perception of travel distance.

Table 3 shows that the average TTT change for CR services was only a marginal increase of 0.55%, while the change from CR to HSR services was a major reduction of 50.88%. However, HSR services development has increased the TTT inequality at the regional scale, which is evidenced by an increased CV of TTT from 0.23 in 2007 to 0.33 in 2016. A decomposition analysis shows that HSR services development only increased the TTT inequity for HSR cities because the TTT inequalities for CR cities under the CR2016 and HSR2016 networks were quite similar.

Figure 2 The TTT patterns of YRD cities, 2007-2016(a: CR2007, b: CR2016, c: Rail2016)

Table 3 TTT and TTC difference between HSR and CR cities, 2007-2016Number of

cities

TTT TTC

Mean CV Mean CV

CR2007 all cities 99 205.47 0.23 151.64 1.13

HSR cities 58 191.24 0.24 219.48 0.87

CR cities 41 225.61 0.18 55.66 1.01

CR2016 all cities 93 206.60 0.24 149.78 1.11

HSR cities 54 191.92 0.26 212.09 0.87

CR cities 39 226.93 0.17 63.51 1.15

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Rail2016 all cities 122 101.49 0.33 296.66 1.41

HSR cities 83 85.41 0.26 406.20 1.14

CR cities 39 135.72 0.20 63.51 1.15

TTC

Figure 3 illustrates the patterns of intercity train frequencies and the aggregated TTC, grouped by a unified legend. This highlights the differences in intensity between CR and HSR services. The three train service networks have globally similar structures but with local variations. CR and HSR services were mainly clustered around hub cities such as Shanghai, Hangzhou, Nanjing, and Suzhou. The busiest intercity train connections were mainly located along two of China’s backbone rails in the YRD sections: the Shanghai-Beijing and Shanghai-Kunming lines. Although the pattern of CR services has undergone little change over a decade, some local variations existed among intercity connections and the city’s TTT. Some intercity CR train connections (e.g., Changzhou-Zhenjiang, Zhenjiang-Wuxi, and Shanghai-Zhenjiang) ran parallel to the main HSR corridor (Nanjing-Shanghai HSR), which decreased the use of CR services by more than 50%. In addition, the number of connections in the northern and southern peripheral areas (e.g., Haining-Hangzhou, Haining-Jiaxing, and Xuzhou-Suzhou) increased by around 30 scheduled trains per day. These findings suggest that CR services were often replaced by HSR services in the YRD’s economically developed intermediate cities, but they continued to function critically in the peripheral areas, and even supplemented HSR services at the sub-regional scale.

Train services have increased connectivity options not only by covering more cities but also by increasing the frequency of average intercity train connections (Table 3). In 2007, there was an average of 8.51 train connections across the 1747 city pairs. By 2016, these numbers had increased to 24.09 and 2691, respectively. In the same period, CR services eliminated 219 intercity train connections. HSR services have enhanced not only the well-established intercity connections of Shanghai-Nanjing and Shanghai-Hangzhou, but also the Nanjing-Hangzhou, Nanjing-Xuzhou, and Hangzhou-Ningbo connections. Thus, HSR development has improved the connectivity status for sub-regional hubs like Hangzhou, Nanjing, and Xuzhou, and has resulted in a polycentric train services structure (Figure 3). These findings corroborate the results of studies that measured polycentric urban development in the YRD through examining the comprehensive transportation network (Liu, Derudder and Wu 2016), and knowledge production and collaboration (Li and Phelps 2016).

Compared with the TTT, the TTC patterns of all rail cities were much more unevenly distributed in both interval years (Table 3). The CV value of rail cities’ TTC increased from 1.13 in 2007 to 1.41 in 2016. This mainly occurred as a result of HSR services development. Such development not only increased the TTC disparity among HSR cities but widened the TTC gap between HSR and CR cities. In contrast, CR services development increased the disparity only among CR cities but helped to level the unevenness among HSR cities.

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Figure 3 Intercity train connections in the YRD, 2007-2016(a: CR2007, b: CR2016, c: Rail2016)

5.2 Patterns of RAS and its changes

Figure 4 shows the RAS pattern and its changes with regard to train services in 2007 and 2016. Cities with higher levels of RAS were mainly located in the southern Jiangsu and north Zhejiang provinces in both years. These cities enjoyed well-maintained CR lines and experienced rapid economic growth. After incorporating train service frequency into the regional accessibility measurement, the RAS pattern is less affected by the geographic distribution of cities than the TTT pattern. Using Shanghai in the CR network in 2007 as an example, its rank in TTT (from low to high) is 16th because of its location at the periphery of YRD, but the city’s TTT ranked 1st for its good connection with the other cities and ranked 1st in RAS. Although cities like Bangbu and Hefei are spatially located in the regional centre and contain rail hubs, their RAS values were around the average level due to their moderate rail service connectivity with other cities in the region.

The introduction of HSR services in the YRD improved RAS by an average of 19.0% but widens the gap between HSR and CR cities. Evidence shows that higher levels of RAS improvement occurred in the mid-eastern HSR cities, where HSR trains ran very frequently, and the network was high quality. Upon first look, Table 4 seems to indicate a huge gap between the average RAS of HSR cities (0.86) and CR cities (0.53) in 2016. However, the difference originated in the CR 2007 services network pattern. Over the last decade, CR services development reduced the RAS gap by 7.6% in relative terms, while HSR services development widened the gap with an increase of 26.9%. In general, HSR services have reduced the unevenness of RAS in the YRD. The CV value of RAS in 2007 and 2016 was 0.35 and 0.25 respectively, representing a 28.6% decrease. The CV value of HSR cities (0.08) was quite small, but it was much higher for CR cities (0.34). In fact, HSR services reduced regional accessibility for cities in general, even though RAS remained unevenly distributed in CR cities.

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Figure 4 RAS and its changes in the YRD, 2007-2016(a: CR2007, b: Rail2016, c: RAS change)

Table 1 RAS difference between HSR and CR cities in the YRD, 2007-2016Number of cities Mean CV

CR2007 all cities 99 0.63 0.35

HSR cities 58 0.74 0.22CR cities 41 0.48 0.40

CR2016 all cities 93 0.61 0.38

HSR cities 54 0.71 0.27

CR cities 39 0.47 0.43

Rail2016 all cities 122 0.75 0.25

HSR cities 83 0.86 0.08

CR cities 39 0.53 0.34

5.3 Results of the two-limit Tobit regression

The two-limit Tobit regression shows that, except for significant parameter estimates, the chi2 tests indicate that each listed model is significant at the 99% confidence level (Table 5). The results show that TTT representing rail infrastructure development was associated with governmental planning referring to population size, while TTC representing train service arrangement was more affected by the dynamics of market signals (i.e., the level of economic development). The patterns of RAS and its two components in 2007 were significant predictors of these in 2016. This shows that HSR service development has enhanced rather than completely restructured the pattern of regional accessibility formed by the CR service network. Moreover, as illustrated in Table 5, the results for the 2007 RAS coefficients (and its two components) for the CR and HSR cities vividly show that CR cities were much more disadvantaged than HSR cities in changing their regional accessibility. The dummy variable for whether or not a city had an HSR station was a significantly important contributor to RAS and its two components in 2016 for all rail cities. The results indicate that the development of HSR network and train services in the YRD was highly selective spatially for cities with better accessibility in the CR network and with higher rank in the administrative hierarchy. This is supported by China’s Mid- to Long-Term Railway Development Plan, in which cities at prefectural

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level and above were prioritised at the first stage of HSR service development.

Controlling for regional accessibility and its two components in 2007 (pre-HSR period) allows us to see that the patterns in 2016 were accounted by the socioeconomic and institutional determinates. Among HSR cities, GDP per capita was the most important predictor for RAS and its two components. This result suggests that the level of economic development was another important factor underlying the Chinese government’s decision regarding where to arrange HSR services. It is generally hypothesised that people in developed cities demand travel and value time at a higher level than people in poor cities (Yin et al. 2015). Among the 2016 TTC pattern for CR cities, the population size was a significant and positive predictor, while the GDP output was a negative and significant predictor. This indicates that some CR cities with better economic performance suffered from worsening regional accessibility, suggesting an uncoordinated issue between rail service and economic development among CR cities. Although CR services had increased slightly for CR cities in 2016, the problem of incoordination persisted in the dimension of TTC. Also, there were no other socioeconomic variables that were significantly correlated with RAS and TTT, after controlling for RAS and TTT in 2007 and city rank. Therefore, by taking CR cities that had already been marginalised by HSR network development into consideration, the analysis suggests that more frequent CR services are needed for CR cities with better economic performance.

Table 5 Two-limit Tobit regression results for TTT, TTC and RAS in 2016TTT (2016) TTC (2016) RAS (2016)

All

cities

CR

cities

HSR

cities

All

cities

CR

cities

HSR

cities

All

cities

CR

cities

HSR

cities

TTT (2007) 0.67*** 1.01*** 0.55***

TTC (2007) 0.72** 0.38** 0.72***

RAS (2007) 0.45*** 0.76*** 0.30***HSR cityor not -0.29*** 0.04** 0.17***

City rank 0.03 -0.06 0.04 0.003 -0.007 -0.03 0.08** 0.004 -0.05**

Population 1.14* 0.08 0.63 0.41 0.49** 0.54 1.06** 2.12 0.61*

GDP -1.04* -0.12 -0.67 -0.08 -0.66* -0.24 1.09** 0.04 -0.57

GDPPC 0.23** -0.24 0.17** 0.13** 0.04 0.15** 0.12* 0.28 0.10**

Constant 0.23*** 0.25 0.01 -0.05 -0.008 -0.0009 0.38** 0.07 0.64**

N 89 31 58 89 31 53 89 31 53

Prob > chi2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Log likelihood 89.18 23.75 85.34 132.54 107.45 75.81 118.34 36.53 111.31

Note: symbols *, **, and *** denote significance levels of 0.1, 0.05 and 0.01, respectively.

6 Conclusion and discussion

As an advanced transport mode, HSR has been expanding rapidly in recent years all over the world. Various studies have furthered our understanding of the uneven pattern of regional accessibility generated by the HSR network in China and Spain, which differs from the patterns generated by HSR corridors in Japan, the UK, and South Korea. Rather than re-emphasising the uneven

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accessibility pattern, this paper investigated how HSR services development has generally restructured regional accessibility patterns, while simultaneously explored how train services, in particular, had varied impacts on CR and HSR cities. This study also incorporated train service frequency into the prevailing speed-dominated accessibility measurement. The changing patterns of regional accessibility were further explained by a set of variables representing the nature of infrastructure, urban socioeconomic performance, and institutions. Given the rapid expansion of the HSR services network, the findings from the YRD carry polity implications for countries that are progressing HSR planning and construction.

HSR services development has improved cities’ RAS and regional inequality but has increased the relative RAS gap between CR and HSR cities in the YRD. The development of HSR services in China was highly spatially selective and was concentrated in those cities with higher accessibility in the CR services network, higher rank in the administrative hierarchy, and better performance in economic development. In contrast to HSR cities, the pattern of train services for CR cities was less related to economic development. In combination with being excluded by HSR development, the issue of ‘peripheralisation of the periphery’ for some CR cities came true in the YRD. As with previous transport innovations, Hall’s (2009) prediction that HSR “will be the maker of some cities, but the breaker of others” came to fruition. HSR services can benefit HSR cities directly while simultaneously widening the accessibility gap between HSR and CR cities. Therefore, the CR cities with good accessibility from previous CR service networks were significantly disadvantaged for a relatively lower accessibility change when compared to those CR cities located close to HSR stations. Hence, when there are plans for an HSR development, policymakers should make sure to minimise its negative effects on some important off-route cities by paying attention to decreases in relative accessibility and potential economic losses.

In the context of massive HSR development in China, it is widely presumed in policy circles that HSR development has significantly changed the accessibility pattern at the regional scale. Many local policy-makers from both small and large cities, therefore, advertise their cities’ relative accessibility advantages when they have a local HSR station, are close in proximity to an HSR station, or are connected to an HSR station. However, the frequency of train services also determines regional accessibility has been ignored intentionally or unintentionally. The findings in this paper suggest that the YRD’s pattern for intercity train service frequency has somewhat been locked in by the decade-old CR service network. Globally the patterns of TTT, TTC, and RAS for rail services between 2007 and 2016 (respectively) are significantly correlated for both CR and HSR cities. It was therefore concluded that HSR services development had enhanced the existing patterns of regional accessibility in the YRD, and that this development has not comprehensively restructured the previous patterns. The stable patterns likely resulted from the persistent spatial economic structure of the YRD during the study period and by the rail agencies’ arrangements of train services for profit considerations. It thus suggests that optimisation of timetables for CR and HSR services can increase regional rail efficiency in the YRD because the megaregion has both an advanced CR and HSR network. Moreover, with the exception of new railway lines, the current pattern of HSR services is highly correlated with CR services. An optimised arrangement of HSR and CR services would reduce their competition. Therefore, future rail management studies and policy research should determine how to coordinate the two services (e.g., arranging CR services to feed into HSR services).

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Currently, several countries have shown interest in developing HSR networks to improve regional accessibility. The gap between the prediction of traffic demand and the capacity of CR services is mainly used to justify the need for HSR development. However, such development policies largely ignore that the impacts of accessibility will be varied for the rail cities that are within HSR services compared to the rail cities that find themselves outside of HSR services. It is important for policymakers to fully evaluate accessibility so that they are well informed about where and how HSR should be developed to achieve the maximum accessibility benefits at the regional scale. Previous studies have conducted ex-ante evaluations of accessibility changes that occurred as a result of major infrastructure development in cities (Zhu, Levinson and Liu 2009, Martínez and Givoni 2012); however, very few studies have evaluated the potential change of the accessibility pattern by jointly considering the potential travel time saved and the change in intercity train connections. Importantly, this paper subsequently illustrates that new HSR planning should: (1) consider both station selection and train services arrangement, and (2) coordinate HSR services with the existing CR service networks.

Our empirical study provides new insights into regional accessibility changes from HSR network development. However, there is scope to expand the investigation. First, regional accessibility changes can be further examined by including the substitution effect of HSR services from other transport modes. HSR services are highly competitive in intercity travel at short-to-medium distances (Givoni 2006, Fu et al. 2012). Even at the medium to long distance, the introduction of HSR services has resulted in airlines reduction in market shares, frequencies and ridership in many cases in China (Fu et al. 2015, Xia and Zhang 2016, Xia and Zhang 2017). The substitution effects of HSR services on airlines will be more strong in the highly populated and developed economic corridors and metropolitan areas (Wang, Xia and Zhang 2017). Therefore, the HSR substitutions should have occurred in a few airport cities in our study area, such as Xuzhou, Ningbo, Wenzhou, Hefei and Shanghai. Thus, these cities would have enjoyed additional HSR services at the expenses air connectivity. This substitution effect means that regional accessibility improvements of these HSR cities would have been overestimated to some extent. Second, due to the lack of availability of passenger data at the city level, socioeconomic indicators are used to represent the potential travel demand in the regression analysis. Passenger volumes and intercity passenger flows would be better proxies to evaluate train services arrangements for future research.

It is worth mentioning that accessibility is only one aspect to consider when ascertaining peoples’ travel choices; in addition to accessibility, travel cost should also be included in these considerations. This is especially important because the cost of HSR services in China is nearly double that of CR services. As HSR continues to grow and the use of the system expands, the substitution effect for HSR on CR services is expected to become more pronounced. Therefore, even though some CR networks have transitioned into freight transportation, the integration and coordination of CR services into the now-dominant HSR services will be an important issue about future sustainable rail transport policies. This study, therefore, suggests that future studies and policy research examine the relationship between the rail services provisions and socioeconomic characteristics of rail users in both HSR and CR networks in order to better inform transport equality. In a wider sense, referring to the questioning of Ng et al. (2018, p.199) in a special issue of Journal of Transport Geography (vol. 71, 2018), “to what extent and under what circumstances” would the development of HSR network (as a government initiative) lead to spatial transformation within transport systems is an important

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research agenda. It also suggests examining how upgraded transport infrastructures and services have affected the economic production network in the context of globalisation. Indeed, theoretical explorations and multi-scale and comparative case studies on the wider impact of economic geography can better inform policy debates on HSR planning and construction.

Finally, unlike many EU countries that chose to upgrade CR stations to adapt to HSR trains, most of China’s HSR stations are newly built and are located in the urban peripheries. CR stations, on the other hand, are mainly located in very accessible urban centres. This suggests that ex-post evaluations of HSR development should consider: the connectivity and accessibility between HSR and CR stations in China. For future research, it would be necessary to analyse intra-city connectivity and accessibility to HSR stations. Under these conditions, a door-to-door approach should be employed to examine the actual intercity accessibility improvement by factoring in the prolonged travel time of intra-city transport caused by station changes.

Acknowledgements

This research is funded by Hallsworth Research Fellowship at the University of Manchester and National Science Foundation of China (41601169 and 41871119). The author would like to thank Cecilia Wong for her insightful suggestions, journal editor Xiaowen Fu and three anonymous reviewers for their comments that have led to a significant improvement of this paper. The original draft was presented at 2017 International Conference on China Urban Development (London). The author would also like to thank the constructive comments from Michael Batty, Pengjun Zhao, James Cheng and Zhi Liu to revise the manuscript.

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